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Activity Number:
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45
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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IMS
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| Abstract - #308125 |
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Title:
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Semiparametric Bivariate Density Estimation with Irregularly Truncated Data
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Author(s):
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Chad Schafer*+
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Companies:
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Carnegie Mellon University
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Address:
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Department of Statistics, Pittsburgh, PA, 15213,
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Keywords:
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semiparametric method ; truncated data ; astronomy
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Abstract:
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This work develops an estimator for the bivariate density given a sample of data truncated to a nonrectangular region. Such inference problems occur in various fields; the motivating application here was a problem in astronomy. The approach is semiparametric, combining a nonparametric local likelihood density estimator with a simple parametric form to account for the dependence of the two random variables. Large sample theory for M-estimators is utilized to approximate the distribution for the estimator. Results are described from the analysis of data from the measurements of quasars.
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